Computers & Internet Books:

Learning from Data

Concepts, Theory, and Methods

Format

Hardback

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Learning from Data by Vladimir Cherkassky
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Description

An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.

Author Biography

Vladimir CherKassky, PhD, is Professor of Electrical and Computer Engineering at the University of Minnesota. He is internationally known for his research on neural networks and statistical learning. Filip Mulier, PhD, has worked in the software field for the last twelve years, part of which has been spent researching, developing, and applying advanced statistical and machine learning methods. He currently holds a project management position.
Release date NZ
August 24th, 2007
Country of Publication
United Kingdom
Edition
2nd Revised edition
Illustrations
Illustrations
Imprint
Wiley-Blackwell (an imprint of John Wiley & Sons Ltd)
Pages
538
Dimensions
165x235x32
ISBN-13
9780471681823
Product ID
3083773

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